Senior Engineer, NCX

🕒 September 1

🌐 Germany, Spain, +1 more countries – Remote

🇩🇪 Germany – Remote 🇪🇸 Spain – Remote 🇵🇱 Poland – Remote

💵 zł292.5k - zł650k / year

⏰ Full Time

🟠 Senior

🧑‍💻 Full-stack Engineer

👻 Ghost score 4%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• Lead NVIDIA Cloud Partner Day 2 operational readiness efforts. • Collaborate with NVIDIA Cloud Partners to establish systems, procedures, automation, and operational methods for managing NVIDIA accelerated infrastructure after deployment and activation. • Develop continuous validation methods for GPU, CPU, storage, and network health across large-scale AI clusters. • Establish telemetry, monitoring, alerting, dashboards, and operational signals across compute, GPU, InfiniBand/RoCE networking, storage, Kubernetes, and AI workloads. • Build automated workflows to detect, isolate, drain, repair, validate, and return unhealthy infrastructure to service. • Implement scalable GPU fleet lifecycle strategies, including driver and firmware administration, Kubernetes node maintenance, OS patching, configuration management, upgrades, and configuration drift identification. • Translate NVIDIA NCP requirements and reference architectures into production practices, validation criteria, runbooks, automation, and measurable operational standards. • Develop health signals, SLOs, metrics, acceptance criteria, and ongoing validation mechanisms for infrastructure reliability and service readiness. • Develop reusable tooling, automation, implementation guides, runbooks, operational playbooks, and reference implementations across multiple NCP environments.

🎯 Requirements

• BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or a related technical field, or equivalent experience. • 8+ years of experience in infrastructure engineering, Site Reliability Engineering, DevOps, cloud platform engineering, systems engineering, or similar roles supporting large-scale production environments. • Strong experience operating Linux-based distributed systems and cloud infrastructure in production. • Deep understanding of Kubernetes, containers, cluster scheduling, and the operational lifecycle of large multi-node environments. • Strong understanding of production observability, including metrics, logging, alerting, dashboards, health checks, and operations guided by service level agreements. • Experience crafting automation for infrastructure lifecycle management, failure detection, remediation, upgrades, and configuration management. • Strong networking fundamentals and experience troubleshooting complex distributed systems across compute, network, and storage layers. • Programming and automation experience using Python, Go, shell scripting, or similar languages. • Experience managing extensive GPU or accelerated computing infrastructure that supports AI training and inference workloads. • Experience with NVIDIA technologies including DGX/HGX systems, CUDA, NVLink/NVSwitch, NVIDIA networking, InfiniBand, RoCE, GPU Operator, Network Operator, or related NVIDIA infrastructure software. • Proven experience collaborating with NVIDIA Cloud Partners, hyperscale cloud providers, managed AI clouds, or extensive service-provider infrastructure and operating SLOs for large-scale compute infrastructure and using operational data to improve availability, performance, and fleet efficiency. • Extensive knowledge of infrastructure observability tools including Prometheus, Grafana, OpenTelemetry, Alertmanager, and scalable telemetry pipelines. • Knowledge of failure modes related to large distributed AI workloads and the infrastructure features necessary to consistently support extended training and production inference.